Video Cut Detector via Adaptive Features using the Frobenius Norm
| Autor: | A. Salam, Fedwa Essannouni, Driss Aboutajdine, Youssef Bendraou |
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| Rok vydání: | 2016 |
| Předmět: |
Discrete mathematics
Detector Matrix norm 020207 software engineering 02 engineering and technology Automatic summarization Thresholding Histogram Singular value decomposition Content (measure theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm Block (data storage) Mathematics |
| Zdroj: | Advances in Visual Computing ISBN: 9783319508313 ISVC (2) |
| DOI: | 10.1007/978-3-319-50832-0_37 |
| Popis: | One of the first and most important steps in content-based video retrieval is the cut detection. Its effectiveness has a major impact towards subsequent high-level applications such as video summarization. In this paper, a robust video cut detector (VCD) based on different theorems related to the singular value decomposition (SVD) is proposed. In our contribution, the Frobenius norm is performed to estimate the appropriate reduced features from the SVD of concatenated block based histograms (CBBH). After that, according to each segment, each frame will be mapped into \(\tilde{k}\)-dimensional vector in the singular space. The classification of continuity values is achieved using an adjusted thresholding technique. Experimental results show the efficiency of our detector, which outperforms recent related methods in detecting the hard cut transitions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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